Introduction to Sampling Methods in Research:


First, you need to understand the difference between a population and a sample, and identify the target population of your research.

The population is the entire group that you want to draw conclusions about.
The sample is the specific group of individuals that you will collect data from.
The population can be defined in terms of geographical location, age, income, and many other characteristics.

Population vs sample
It can be very broad or quite narrow:
However, It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.

Sampling frame
The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Example: Sampling frame
You are doing research on working conditions at Company X. Your population is all 1000 employees of the company. Your sampling frame is the company’s HR database which lists the names and contact details of every employee.

Sample size
The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis.
There are two types of sampling methods:
Probability sampling methods
Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

Probability sampling
1. Simple random sampling

2. Systematic sampling

3. Stratified sampling

4. Cluster sampling

Non-probability sampling methods:
In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias.
That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

There are 4 main types of Non probability sampling:
1. Convenience sampling
2. Voluntary response sampling
3. Purposive sampling
4. Snowball sampling

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VIDEO KEY WORDS:
SAMPLING METHODS, PROBABILITY SAMPLING, SIMPLE RANDOM SAMPLING, SAMPLING TECHNIQUES, TYPES OF SAMPLING METHODS, SAMPLING METHOD FOR QUALITATIVE RESEARCH, SAMPLING METHODS AND BIAS WITH SURVEYS, SAMPLING METHOD IN HINDI, INTRODUCTION TO SAMPLING METHOD, SAMPLING METHODS IN RESEARCH, INTRODUCTION TO SAMPLING METHODS IN RESEARCH, NON PROBABILITY SAMPLING, NON PROBABILITY SAMPLING METHOD, CLUSTER SAMPLING, SNOWBALL SAMPLING, STRATIFIED SAMPLING, PURPOSIVE SAMPLING, SYSTEMATIC SAMPLING, CONVENIENCE SAMPLING, QUOTA SAMPLING, PUBLIC HEALTH LECTURE SERIES

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